Comparing the Rates of Adaptive Change in Species Across Biogeographic Gradients
Bibliographic record
Abstract
The process of adaptive evolution allows a species to persist with specific traits tailored to their environment. The rate at which a population adapts can change based on the varying environmental abiotic and biotic factors of their location. Environmental characteristics such as the amount of available energy, elevation, temperature and degree of human disturbance thus affect the rates of adaptive evolution across biogeographic gradients. In this study, I used data on adaptive rates for terrestrial species and aimed to determine if the rates of adaptive evolution showed patterns related to spatial biogeographic patterns and environmental factors such as elevation, evapotranspiration, precipitation and temperature. Additionally, I tested whether the rates of adaption changed based on these specific environmental factors and a human density. The results of my data analysis detected that the rates of adaptation showed good variation in relation to spatial scale patterning but no significant correlation patterns between the tested environmental variables and the rates of adaptive evolution were detected. This result did not support my hypothesis that the rates of adaptive change would vary through space with environmental gradients. Many potential factors may have caused this inconclusive result. Further research into this topic would be useful to understand and predict the adaptive evolution of species’ in varying environmental gradients. I propose that studies on this data should aim to utilize more of the available samples within this public dataset such as the aquatic species. Additionally, further research may benefit from narrowing the study size based on location or taxa for significant relationships to be accurately detected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".